Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory208.6 B

Variable types

Numeric16
Categorical10

Alerts

marque_voiture has a high cardinality: 142 distinct valuesHigh cardinality
etat_de_route is highly overall correlated with empattement and 2 other fieldsHigh correlation
empattement is highly overall correlated with etat_de_route and 10 other fieldsHigh correlation
longueur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
largeur_voiture is highly overall correlated with empattement and 9 other fieldsHigh correlation
hauteur_voiture is highly overall correlated with etat_de_route and 3 other fieldsHigh correlation
poids_vehicule is highly overall correlated with empattement and 8 other fieldsHigh correlation
taille_moteur is highly overall correlated with empattement and 11 other fieldsHigh correlation
taux_alésage is highly overall correlated with empattement and 8 other fieldsHigh correlation
course is highly overall correlated with emplacement_moteurHigh correlation
taux_compression is highly overall correlated with carburant and 3 other fieldsHigh correlation
chevaux is highly overall correlated with empattement and 11 other fieldsHigh correlation
tour_moteur is highly overall correlated with carburantHigh correlation
consommation_ville is highly overall correlated with longueur_voiture and 7 other fieldsHigh correlation
consommation_autoroute is highly overall correlated with empattement and 9 other fieldsHigh correlation
prix is highly overall correlated with empattement and 8 other fieldsHigh correlation
carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
turbo is highly overall correlated with taux_compression and 1 other fieldsHigh correlation
nombre_portes is highly overall correlated with etat_de_route and 2 other fieldsHigh correlation
type_vehicule is highly overall correlated with nombre_portesHigh correlation
emplacement_moteur is highly overall correlated with empattement and 3 other fieldsHigh correlation
type_moteur is highly overall correlated with taille_moteur and 2 other fieldsHigh correlation
nombre_cylindres is highly overall correlated with largeur_voiture and 5 other fieldsHigh correlation
systeme_carburant is highly overall correlated with taux_compression and 2 other fieldsHigh correlation
carburant is highly imbalanced (53.9%)Imbalance
emplacement_moteur is highly imbalanced (89.0%)Imbalance
nombre_cylindres is highly imbalanced (57.6%)Imbalance
car_ID is uniformly distributedUniform
marque_voiture is uniformly distributedUniform
car_ID has unique valuesUnique
etat_de_route has 67 (32.7%) zerosZeros

Reproduction

Analysis started2023-04-25 09:04:50.595970
Analysis finished2023-04-25 09:05:37.312945
Duration46.72 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

car_ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:37.458841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.322565
Coefficient of variation (CV)0.57594723
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.1667
MonotonicityStrictly increasing
2023-04-25T11:05:37.765706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
142 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%

etat_de_route
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83414634
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Negative25
Negative (%)12.2%
Memory size1.7 KiB
2023-04-25T11:05:37.924044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2453068
Coefficient of variation (CV)1.4929117
Kurtosis-0.67627136
Mean0.83414634
Median Absolute Deviation (MAD)1
Skewness0.21107227
Sum171
Variance1.5507891
MonotonicityNot monotonic
2023-04-25T11:05:38.080636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
-1 22
 
10.7%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 22
 
10.7%
0 67
32.7%
1 54
26.3%
2 32
15.6%
3 27
13.2%
ValueCountFrequency (%)
3 27
13.2%
2 32
15.6%
1 54
26.3%
0 67
32.7%
-1 22
 
10.7%
-2 3
 
1.5%

marque_voiture
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct142
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
peugeot 504
 
6
toyota corolla
 
6
toyota corona
 
6
subaru dl
 
4
honda civic
 
3
Other values (137)
180 

Length

Max length31
Median length24
Mean length14.204878
Min length6

Characters and Unicode

Total characters2912
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)49.8%

Sample

1st rowalfa-romeo giulia
2nd rowalfa-romeo stelvio
3rd rowalfa-romeo Quadrifoglio
4th rowaudi 100 ls
5th rowaudi 100ls

Common Values

ValueCountFrequency (%)
peugeot 504 6
 
2.9%
toyota corolla 6
 
2.9%
toyota corona 6
 
2.9%
subaru dl 4
 
2.0%
honda civic 3
 
1.5%
toyota mark ii 3
 
1.5%
mitsubishi g4 3
 
1.5%
volkswagen rabbit 3
 
1.5%
mitsubishi outlander 3
 
1.5%
mitsubishi mirage g4 3
 
1.5%
Other values (132) 165
80.5%

Length

2023-04-25T11:05:38.298333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 32
 
6.6%
nissan 18
 
3.7%
mazda 17
 
3.5%
mitsubishi 13
 
2.7%
honda 13
 
2.7%
corolla 12
 
2.5%
subaru 12
 
2.5%
volkswagen 12
 
2.5%
volvo 11
 
2.3%
peugeot 11
 
2.3%
Other values (162) 337
69.1%

Most occurring characters

ValueCountFrequency (%)
285
 
9.8%
a 261
 
9.0%
o 245
 
8.4%
t 167
 
5.7%
e 160
 
5.5%
s 155
 
5.3%
i 147
 
5.0%
l 141
 
4.8%
r 130
 
4.5%
c 125
 
4.3%
Other values (35) 1096
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2397
82.3%
Space Separator 285
 
9.8%
Decimal Number 179
 
6.1%
Open Punctuation 13
 
0.4%
Dash Punctuation 13
 
0.4%
Close Punctuation 13
 
0.4%
Uppercase Letter 12
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 261
 
10.9%
o 245
 
10.2%
t 167
 
7.0%
e 160
 
6.7%
s 155
 
6.5%
i 147
 
6.1%
l 141
 
5.9%
r 130
 
5.4%
c 125
 
5.2%
u 125
 
5.2%
Other values (15) 741
30.9%
Decimal Number
ValueCountFrequency (%)
0 44
24.6%
4 37
20.7%
1 23
12.8%
2 21
11.7%
5 18
10.1%
9 12
 
6.7%
6 12
 
6.7%
3 10
 
5.6%
7 2
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M 4
33.3%
D 3
25.0%
Q 1
 
8.3%
U 1
 
8.3%
X 1
 
8.3%
V 1
 
8.3%
C 1
 
8.3%
Space Separator
ValueCountFrequency (%)
285
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2409
82.7%
Common 503
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 261
 
10.8%
o 245
 
10.2%
t 167
 
6.9%
e 160
 
6.6%
s 155
 
6.4%
i 147
 
6.1%
l 141
 
5.9%
r 130
 
5.4%
c 125
 
5.2%
u 125
 
5.2%
Other values (22) 753
31.3%
Common
ValueCountFrequency (%)
285
56.7%
0 44
 
8.7%
4 37
 
7.4%
1 23
 
4.6%
2 21
 
4.2%
5 18
 
3.6%
( 13
 
2.6%
- 13
 
2.6%
) 13
 
2.6%
9 12
 
2.4%
Other values (3) 24
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285
 
9.8%
a 261
 
9.0%
o 245
 
8.4%
t 167
 
5.7%
e 160
 
5.5%
s 155
 
5.3%
i 147
 
5.0%
l 141
 
4.8%
r 130
 
4.5%
c 125
 
4.3%
Other values (35) 1096
37.6%

carburant
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
185 
diesel
20 

Length

Max length6
Median length3
Mean length3.2926829
Min length3

Characters and Unicode

Total characters675
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Length

2023-04-25T11:05:38.496963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:38.695784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
gas 185
90.2%
diesel 20
 
9.8%

Most occurring characters

ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 675
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 205
30.4%
g 185
27.4%
a 185
27.4%
e 40
 
5.9%
d 20
 
3.0%
i 20
 
3.0%
l 20
 
3.0%

turbo
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
168 
turbo
37 

Length

Max length5
Median length3
Mean length3.3609756
Min length3

Characters and Unicode

Total characters689
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Length

2023-04-25T11:05:38.832767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:38.989683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
std 168
82.0%
turbo 37
 
18.0%

Most occurring characters

ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 689
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 689
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 205
29.8%
s 168
24.4%
d 168
24.4%
u 37
 
5.4%
r 37
 
5.4%
b 37
 
5.4%
o 37
 
5.4%

nombre_portes
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
115 
two
90 

Length

Max length4
Median length4
Mean length3.5609756
Min length3

Characters and Unicode

Total characters730
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Length

2023-04-25T11:05:39.119004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:39.266041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 115
56.1%
two 90
43.9%

Most occurring characters

ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 730
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 205
28.1%
f 115
15.8%
u 115
15.8%
r 115
15.8%
t 90
12.3%
w 90
12.3%

type_vehicule
Categorical

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
96 
hatchback
70 
wagon
25 
hardtop
 
8
convertible
 
6

Length

Max length11
Median length5
Mean length6.6195122
Min length5

Characters and Unicode

Total characters1357
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Length

2023-04-25T11:05:39.389270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:39.556222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
sedan 96
46.8%
hatchback 70
34.1%
wagon 25
 
12.2%
hardtop 8
 
3.9%
convertible 6
 
2.9%

Most occurring characters

ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1357
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1357
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269
19.8%
h 148
10.9%
c 146
10.8%
n 127
9.4%
e 108
8.0%
d 104
 
7.7%
s 96
 
7.1%
t 84
 
6.2%
b 76
 
5.6%
k 70
 
5.2%
Other values (8) 129
9.5%

roues_motrices
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
120 
rwd
76 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters615
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Length

2023-04-25T11:05:39.699380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:39.856193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
fwd 120
58.5%
rwd 76
37.1%
4wd 9
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 606
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 606
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 205
33.8%
d 205
33.8%
f 120
19.8%
r 76
 
12.5%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 205
33.3%
d 205
33.3%
f 120
19.5%
r 76
 
12.4%
4 9
 
1.5%

emplacement_moteur
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
202 
rear
 
3

Length

Max length5
Median length5
Mean length4.9853659
Min length4

Characters and Unicode

Total characters1022
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Length

2023-04-25T11:05:39.978664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:40.132014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
front 202
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1022
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1022
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 208
20.4%
f 202
19.8%
o 202
19.8%
n 202
19.8%
t 202
19.8%
e 3
 
0.3%
a 3
 
0.3%

empattement
Real number (ℝ)

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.756585
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:40.330010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.0217757
Coefficient of variation (CV)0.060975941
Kurtosis1.0170389
Mean98.756585
Median Absolute Deviation (MAD)2.7
Skewness1.0502138
Sum20245.1
Variance36.261782
MonotonicityNot monotonic
2023-04-25T11:05:40.519298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.2%
93.7 20
 
9.8%
95.7 13
 
6.3%
96.5 8
 
3.9%
97.3 7
 
3.4%
98.4 7
 
3.4%
104.3 6
 
2.9%
100.4 6
 
2.9%
107.9 6
 
2.9%
98.8 6
 
2.9%
Other values (43) 105
51.2%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.4%
93.3 1
 
0.5%
93.7 20
9.8%
94.3 1
 
0.5%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.0%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.4%
108 1
 
0.5%
107.9 6
2.9%
106.7 1
 
0.5%

longueur_voiture
Real number (ℝ)

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04927
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:40.729833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.337289
Coefficient of variation (CV)0.070883886
Kurtosis-0.082894853
Mean174.04927
Median Absolute Deviation (MAD)6.9
Skewness0.15595377
Sum35680.1
Variance152.20869
MonotonicityNot monotonic
2023-04-25T11:05:40.955724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.3%
188.8 11
 
5.4%
171.7 7
 
3.4%
186.7 7
 
3.4%
166.3 7
 
3.4%
165.3 6
 
2.9%
177.8 6
 
2.9%
176.2 6
 
2.9%
186.6 6
 
2.9%
172 5
 
2.4%
Other values (65) 129
62.9%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.3%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.0%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

largeur_voiture
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.907805
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:41.172093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1452039
Coefficient of variation (CV)0.032548556
Kurtosis0.70276424
Mean65.907805
Median Absolute Deviation (MAD)1.4
Skewness0.9040035
Sum13511.1
Variance4.6018996
MonotonicityNot monotonic
2023-04-25T11:05:41.365088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
63.8 24
 
11.7%
66.5 23
 
11.2%
65.4 15
 
7.3%
63.6 11
 
5.4%
64.4 10
 
4.9%
68.4 10
 
4.9%
64 9
 
4.4%
65.5 8
 
3.9%
65.2 7
 
3.4%
64.2 6
 
2.9%
Other values (34) 82
40.0%
ValueCountFrequency (%)
60.3 1
 
0.5%
61.8 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.4%
63.8 24
11.7%
63.9 3
 
1.5%
64 9
 
4.4%
64.1 2
 
1.0%
64.2 6
 
2.9%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.0%

hauteur_voiture
Real number (ℝ)

Distinct49
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.724878
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:41.697065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.443522
Coefficient of variation (CV)0.045482132
Kurtosis-0.44381237
Mean53.724878
Median Absolute Deviation (MAD)1.6
Skewness0.063122732
Sum11013.6
Variance5.9707996
MonotonicityNot monotonic
2023-04-25T11:05:41.888715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
6.8%
52 12
 
5.9%
55.7 12
 
5.9%
54.1 10
 
4.9%
54.5 10
 
4.9%
55.5 9
 
4.4%
56.7 8
 
3.9%
54.3 8
 
3.9%
52.6 7
 
3.4%
56.1 7
 
3.4%
Other values (39) 108
52.7%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.0%
49.7 3
 
1.5%
50.2 6
2.9%
50.5 2
 
1.0%
50.6 5
 
2.4%
50.8 14
6.8%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.0%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
3.9%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.4%

poids_vehicule
Real number (ℝ)

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5659
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:42.085913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802
Coefficient of variation (CV)0.20374361
Kurtosis-0.042853766
Mean2555.5659
Median Absolute Deviation (MAD)386
Skewness0.68139819
Sum523891
Variance271107.87
MonotonicityNot monotonic
2023-04-25T11:05:42.286590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.0%
1918 3
 
1.5%
2275 3
 
1.5%
1989 3
 
1.5%
2410 2
 
1.0%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
Other values (161) 180
87.8%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

type_moteur
Categorical

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
148 
ohcf
15 
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1268293
Min length1

Characters and Unicode

Total characters641
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Length

2023-04-25T11:05:42.452981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:42.633338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
ohc 148
72.2%
ohcf 15
 
7.3%
ohcv 13
 
6.3%
dohc 12
 
5.9%
l 12
 
5.9%
rotor 4
 
2.0%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 197
30.7%
h 189
29.5%
c 189
29.5%
f 15
 
2.3%
v 14
 
2.2%
d 13
 
2.0%
l 12
 
1.9%
r 8
 
1.2%
t 4
 
0.6%

nombre_cylindres
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
159 
six
24 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.902439
Min length3

Characters and Unicode

Total characters800
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2023-04-25T11:05:42.786380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:42.971599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
four 159
77.6%
six 24
 
11.7%
five 11
 
5.4%
eight 5
 
2.4%
two 4
 
2.0%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 170
21.2%
o 163
20.4%
r 160
20.0%
u 159
19.9%
i 40
 
5.0%
s 24
 
3.0%
x 24
 
3.0%
e 20
 
2.5%
v 12
 
1.5%
t 11
 
1.4%
Other values (4) 17
 
2.1%

taille_moteur
Real number (ℝ)

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90732
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:43.155212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.642693
Coefficient of variation (CV)0.32813469
Kurtosis5.3056821
Mean126.90732
Median Absolute Deviation (MAD)23
Skewness1.947655
Sum26016
Variance1734.1139
MonotonicityNot monotonic
2023-04-25T11:05:43.380633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
122 15
 
7.3%
92 15
 
7.3%
97 14
 
6.8%
98 14
 
6.8%
108 13
 
6.3%
90 12
 
5.9%
110 12
 
5.9%
109 8
 
3.9%
120 7
 
3.4%
141 7
 
3.4%
Other values (34) 88
42.9%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
5.9%
91 5
 
2.4%
92 15
7.3%
97 14
6.8%
98 14
6.8%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.0%
181 6
2.9%
Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
94 
2bbl
66 
idi
20 
1bbl
11 
spdi
 
9
Other values (3)
 
5

Length

Max length4
Median length4
Mean length3.897561
Min length3

Characters and Unicode

Total characters799
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Length

2023-04-25T11:05:43.571979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-25T11:05:43.758803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 94
45.9%
2bbl 66
32.2%
idi 20
 
9.8%
1bbl 11
 
5.4%
spdi 9
 
4.4%
4bbl 3
 
1.5%
mfi 1
 
0.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 719
90.0%
Decimal Number 80
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Decimal Number
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 719
90.0%
Common 80
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 160
22.3%
i 145
20.2%
p 104
14.5%
f 96
13.4%
m 95
13.2%
l 80
11.1%
d 29
 
4.0%
s 10
 
1.4%
Common
ValueCountFrequency (%)
2 66
82.5%
1 11
 
13.8%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 160
20.0%
i 145
18.1%
p 104
13.0%
f 96
12.0%
m 95
11.9%
l 80
10.0%
2 66
8.3%
d 29
 
3.6%
1 11
 
1.4%
s 10
 
1.3%

taux_alésage
Real number (ℝ)

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3297561
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:43.937987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.27084371
Coefficient of variation (CV)0.081340404
Kurtosis-0.78504183
Mean3.3297561
Median Absolute Deviation (MAD)0.26
Skewness0.020156418
Sum682.6
Variance0.073356313
MonotonicityNot monotonic
2023-04-25T11:05:44.116134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3.62 23
 
11.2%
3.19 20
 
9.8%
3.15 15
 
7.3%
3.03 12
 
5.9%
2.97 12
 
5.9%
3.46 9
 
4.4%
3.31 8
 
3.9%
3.43 8
 
3.9%
3.78 8
 
3.9%
3.27 7
 
3.4%
Other values (28) 83
40.5%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.4%
2.92 1
 
0.5%
2.97 12
5.9%
2.99 1
 
0.5%
3.01 5
2.4%
3.03 12
5.9%
3.05 6
2.9%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
3.9%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.4%
3.63 2
 
1.0%
3.62 23
11.2%
3.61 1
 
0.5%
3.6 1
 
0.5%

course
Real number (ℝ)

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2554146
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:44.285524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31359701
Coefficient of variation (CV)0.096330898
Kurtosis2.1743964
Mean3.2554146
Median Absolute Deviation (MAD)0.14
Skewness-0.68970458
Sum667.36
Variance0.098343087
MonotonicityNot monotonic
2023-04-25T11:05:44.444387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.4 20
 
9.8%
3.23 14
 
6.8%
3.15 14
 
6.8%
3.03 14
 
6.8%
3.39 13
 
6.3%
2.64 11
 
5.4%
3.29 9
 
4.4%
3.35 9
 
4.4%
3.46 8
 
3.9%
3.11 6
 
2.9%
Other values (27) 87
42.4%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.4%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
6.8%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 3
 
1.5%
3.86 4
2.0%
3.64 5
2.4%
3.58 6
2.9%
3.54 4
2.0%
3.52 5
2.4%
3.5 6
2.9%
3.47 4
2.0%
3.46 8
3.9%

taux_compression
Real number (ℝ)

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142537
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:44.606130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.9720403
Coefficient of variation (CV)0.39162199
Kurtosis5.2330543
Mean10.142537
Median Absolute Deviation (MAD)0.4
Skewness2.6108625
Sum2079.22
Variance15.777104
MonotonicityNot monotonic
2023-04-25T11:05:44.779757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 46
22.4%
9.4 26
12.7%
8.5 14
 
6.8%
9.5 13
 
6.3%
9.3 11
 
5.4%
8.7 9
 
4.4%
8 8
 
3.9%
9.2 8
 
3.9%
7 7
 
3.4%
8.6 5
 
2.4%
Other values (22) 58
28.3%
ValueCountFrequency (%)
7 7
3.4%
7.5 5
 
2.4%
7.6 4
 
2.0%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
3.9%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.4%
8.5 14
6.8%
ValueCountFrequency (%)
23 5
2.4%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.0%
21 5
2.4%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

chevaux
Real number (ℝ)

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.11707
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:44.991020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.544167
Coefficient of variation (CV)0.37980483
Kurtosis2.6840062
Mean104.11707
Median Absolute Deviation (MAD)25
Skewness1.4053102
Sum21344
Variance1563.7411
MonotonicityNot monotonic
2023-04-25T11:05:45.202162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.3%
70 11
 
5.4%
69 10
 
4.9%
116 9
 
4.4%
110 8
 
3.9%
95 7
 
3.4%
114 6
 
2.9%
160 6
 
2.9%
101 6
 
2.9%
62 6
 
2.9%
Other values (49) 117
57.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
2.9%
64 1
 
0.5%
68 19
9.3%
69 10
4.9%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
1.0%
182 3
1.5%
176 2
1.0%
175 1
 
0.5%
162 2
1.0%
161 2
1.0%

tour_moteur
Real number (ℝ)

Distinct23
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.122
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:45.382491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.98564
Coefficient of variation (CV)0.093068155
Kurtosis0.086755856
Mean5125.122
Median Absolute Deviation (MAD)300
Skewness0.075158722
Sum1050650
Variance227515.3
MonotonicityNot monotonic
2023-04-25T11:05:45.533855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
18.0%
4800 36
17.6%
5000 27
13.2%
5200 23
11.2%
5400 13
 
6.3%
6000 9
 
4.4%
4500 7
 
3.4%
5800 7
 
3.4%
5250 7
 
3.4%
5100 5
 
2.4%
Other values (13) 34
16.6%
ValueCountFrequency (%)
4150 5
 
2.4%
4200 5
 
2.4%
4250 3
 
1.5%
4350 4
 
2.0%
4400 3
 
1.5%
4500 7
 
3.4%
4650 1
 
0.5%
4750 4
 
2.0%
4800 36
17.6%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.4%
5900 3
 
1.5%
5800 7
 
3.4%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
18.0%
5400 13
 
6.3%
5300 1
 
0.5%
5250 7
 
3.4%

consommation_ville
Real number (ℝ)

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.219512
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:45.696223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.5421417
Coefficient of variation (CV)0.25940794
Kurtosis0.57864834
Mean25.219512
Median Absolute Deviation (MAD)5
Skewness0.66370403
Sum5170
Variance42.799617
MonotonicityNot monotonic
2023-04-25T11:05:45.861199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
13.7%
19 27
13.2%
24 22
10.7%
27 14
 
6.8%
17 13
 
6.3%
26 12
 
5.9%
23 12
 
5.9%
21 8
 
3.9%
25 8
 
3.9%
30 8
 
3.9%
Other values (19) 53
25.9%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
2.9%
17 13
6.3%
18 3
 
1.5%
19 27
13.2%
20 3
 
1.5%
21 8
 
3.9%
22 4
 
2.0%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.4%
37 6
2.9%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

consommation_autoroute
Real number (ℝ)

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75122
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:46.059557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8864431
Coefficient of variation (CV)0.22394049
Kurtosis0.44007038
Mean30.75122
Median Absolute Deviation (MAD)5
Skewness0.53999719
Sum6304
Variance47.423099
MonotonicityNot monotonic
2023-04-25T11:05:46.273763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.3%
38 17
 
8.3%
24 17
 
8.3%
30 16
 
7.8%
32 16
 
7.8%
34 14
 
6.8%
37 13
 
6.3%
28 13
 
6.3%
29 10
 
4.9%
33 9
 
4.4%
Other values (20) 61
29.8%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
3.9%
23 7
 
3.4%
24 17
8.3%
25 19
9.3%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.0%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.3%

prix
Real number (ℝ)

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.702
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-04-25T11:05:46.467984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.849
Coefficient of variation (CV)0.60171937
Kurtosis3.0516511
Mean13276.702
Median Absolute Deviation (MAD)3306
Skewness1.777678
Sum2721724
Variance63821708
MonotonicityNot monotonic
2023-04-25T11:05:46.830474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8921 2
 
1.0%
9279 2
 
1.0%
7898 2
 
1.0%
8916 2
 
1.0%
7775 2
 
1.0%
8845 2
 
1.0%
7295 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
Other values (179) 185
90.2%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2023-04-25T11:05:32.951670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:52.673677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.932381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.488344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.427015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:04.639718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.407118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.017303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:13.059838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.577618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.899189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.186193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.453628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.617792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.156531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:30.051767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:33.142999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:52.866744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.085832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.676800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.626171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:04.860152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.610488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.160630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:13.263866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.725185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.056488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.350852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.592159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.758326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.299638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:30.305512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:33.322939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:53.038393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.218051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.867710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.802221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.026821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.743736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.289992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:13.521728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.876666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.223264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.486809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.722625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.888702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.445261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:30.536767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:33.483458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:53.249690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.365467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.043665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.964799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.265397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.879900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.460793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:13.712962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.022113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.358765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.636139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.860945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.070788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.693356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:30.736135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:33.685412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:53.431845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.519915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.228699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:02.184051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.428126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.029017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.631505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:13.882736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.166996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.491765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.779123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.001983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.217258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.869555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:30.944956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:33.924319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:53.617968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.679433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.398447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:02.364484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.577538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.239500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.778599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.056533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.318169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.615782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.926031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.129618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.375629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.046942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.106370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:34.090133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:53.797306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:56.830677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.556411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:02.529548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.715219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.432682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:10.965503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.203428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.461701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.775069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.060880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.257652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.600355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.206109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.315752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:34.264784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:54.164606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.064999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.705715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:02.831705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:05.884080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.570801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:11.157272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.338294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.617715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:18.908601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.195771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.408045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.796996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.435872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.511881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:34.498837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:54.377680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.214550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:59.915282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:02.973707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.035640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.733451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:11.538975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.474226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.765244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.036713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.324113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.530971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:25.931982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.581220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.664839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:34.757612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:54.577254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.365193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:00.064302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:03.138761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.213568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:08.947063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:11.767751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.618733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:16.911597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.165715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.475888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.679091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.070594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.751286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.818031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:34.993005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:54.807700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.491924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:00.265499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:03.302677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.348701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.078707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:11.937445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.762452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.043864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.416982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.600837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.799416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.199439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:28.898052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:31.978456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:35.342972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.061874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.642174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:00.448605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:03.469954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.536459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.226601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:12.182017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:14.904616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.193372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.548446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.738958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:23.943143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.331566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:29.076770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:32.162166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:35.571845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.209938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:57.798913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:00.674693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:03.635668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.757064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.362142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:12.404569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.046039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.323659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.668972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.871816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.063768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.456146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:29.267942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:32.299973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:35.723501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.374950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.011584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:00.899170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:03.859818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:06.915243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.487646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:12.553135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.162261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.454570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.782040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:21.996503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.190400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.575066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:29.437840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:32.438428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:35.886421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.538818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.190737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.087363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:04.116701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.065679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.678929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:12.709130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.299627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.602776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:19.910415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.141721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.318933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:26.875606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:29.673755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:32.613771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:36.074227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:55.719904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:04:58.340089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:01.253400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:04.370079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:07.256970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:09.855439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:12.879438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:15.435382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:17.755741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:20.046829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:22.299323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:24.466344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:27.014502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:29.881425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-25T11:05:32.781829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-25T11:05:47.038392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
car_IDetat_de_routeempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetaille_moteurtaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprixcarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurtype_moteurnombre_cylindressysteme_carburant
car_ID1.000-0.1570.1970.1550.1490.2630.1240.0890.273-0.1600.1510.005-0.2300.0560.0210.0200.2890.2610.3430.1770.4240.3060.4120.2790.385
etat_de_route-0.1571.000-0.538-0.396-0.254-0.523-0.256-0.177-0.170-0.0190.023-0.0100.282-0.0180.053-0.1450.2170.1850.6840.3340.2660.2720.2220.1600.266
empattement0.197-0.5381.0000.9120.8120.6330.7650.6480.5370.227-0.1260.505-0.312-0.493-0.5390.6820.3410.3100.4450.3340.4170.5680.3530.3160.226
longueur_voiture0.155-0.3960.9121.0000.8880.5250.8900.7830.6390.187-0.1930.661-0.269-0.670-0.6980.8040.1100.2070.3650.2410.4090.0000.3170.3560.326
largeur_voiture0.149-0.2540.8120.8881.0000.3500.8640.7710.6100.240-0.1460.689-0.199-0.688-0.7010.8110.2330.3010.3050.1280.4030.1600.3690.5670.246
hauteur_voiture0.263-0.5230.6330.5250.3501.0000.3460.2000.216-0.0180.0000.011-0.296-0.069-0.1330.2430.2770.2370.5410.4970.3600.2720.3880.3500.292
poids_vehicule0.124-0.2560.7650.8900.8640.3461.0000.8780.7020.163-0.2190.808-0.236-0.813-0.8340.9090.3050.3750.2740.2300.4560.1000.3270.4820.292
taille_moteur0.089-0.1770.6480.7830.7710.2000.8781.0000.7010.292-0.2350.817-0.273-0.730-0.7210.8260.1570.2710.2070.2020.4690.6190.5270.6420.333
taux_alésage0.273-0.1700.5370.6390.6100.2160.7020.7011.000-0.083-0.1600.639-0.298-0.609-0.6150.6440.1680.3350.1630.1510.4340.3270.4180.2580.345
course-0.160-0.0190.2270.1870.240-0.0180.1630.292-0.0831.000-0.0700.130-0.074-0.030-0.0300.1110.3750.2650.1320.1510.3380.6150.4040.2390.303
taux_compression0.1510.023-0.126-0.193-0.1460.000-0.219-0.235-0.160-0.0701.000-0.353-0.0220.4790.445-0.1740.9930.5540.1860.0480.1140.0000.3380.5210.518
chevaux0.005-0.0100.5050.6610.6890.0110.8080.8170.6390.130-0.3531.0000.113-0.911-0.8860.8550.2190.3430.1710.1890.4020.8430.5140.5640.317
tour_moteur-0.2300.282-0.312-0.269-0.199-0.296-0.236-0.273-0.298-0.074-0.0220.1131.000-0.131-0.057-0.0660.5940.3110.2440.0740.2420.4480.3590.2830.363
consommation_ville0.056-0.018-0.493-0.670-0.688-0.069-0.813-0.730-0.609-0.0300.479-0.911-0.1311.0000.968-0.8290.3890.1860.0030.0000.3800.1100.2090.4240.304
consommation_autoroute0.0210.053-0.539-0.698-0.701-0.133-0.834-0.721-0.615-0.0300.445-0.886-0.0570.9681.000-0.8230.3360.3190.1190.0000.4370.1010.3250.5000.341
prix0.020-0.1450.6820.8040.8110.2430.9090.8260.6440.111-0.1740.855-0.066-0.829-0.8231.0000.3380.4070.0000.2290.4510.4510.2880.4290.290
carburant0.2890.2170.3410.1100.2330.2770.3050.1570.1680.3750.9930.2190.5940.3890.3360.3381.0000.3740.1610.1730.0880.0000.2500.1550.985
turbo0.2610.1850.3100.2070.3010.2370.3750.2710.3350.2650.5540.3430.3110.1860.3190.4070.3741.0000.0000.0000.1180.0000.1500.1960.610
nombre_portes0.3430.6840.4450.3650.3050.5410.2740.2070.1630.1320.1860.1710.2440.0030.1190.0000.1610.0001.0000.7410.0500.0670.2000.1340.245
type_vehicule0.1770.3340.3340.2410.1280.4970.2300.2020.1510.1510.0480.1890.0740.0000.0000.2290.1730.0000.7411.0000.2140.4380.1320.0680.144
roues_motrices0.4240.2660.4170.4090.4030.3600.4560.4690.4340.3380.1140.4020.2420.3800.4370.4510.0880.1180.0500.2141.0000.1240.4250.3360.387
emplacement_moteur0.3060.2720.5680.0000.1600.2720.1000.6190.3270.6150.0000.8430.4480.1100.1010.4510.0000.0000.0670.4380.1241.0000.3990.2880.000
type_moteur0.4120.2220.3530.3170.3690.3880.3270.5270.4180.4040.3380.5140.3590.2090.3250.2880.2500.1500.2000.1320.4250.3991.0000.5460.377
nombre_cylindres0.2790.1600.3160.3560.5670.3500.4820.6420.2580.2390.5210.5640.2830.4240.5000.4290.1550.1960.1340.0680.3360.2880.5461.0000.373
systeme_carburant0.3850.2660.2260.3260.2460.2920.2920.3330.3450.3030.5180.3170.3630.3040.3410.2900.9850.6100.2450.1440.3870.0000.3770.3731.000

Missing values

2023-04-25T11:05:36.426753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-25T11:05:37.057486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

car_IDetat_de_routemarque_voiturecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprix
013alfa-romeo giuliagasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495
123alfa-romeo stelviogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500
231alfa-romeo Quadrifogliogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500
342audi 100 lsgasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950
452audi 100lsgasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450
562audi foxgasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250
671audi 100lsgasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710
781audi 5000gasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920
891audi 4000gasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875
9100audi 5000s (diesel)gasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859
car_IDetat_de_routemarque_voiturecarburantturbonombre_portestype_vehiculeroues_motricesemplacement_moteurempattementlongueur_voiturelargeur_voiturehauteur_voiturepoids_vehiculetype_moteurnombre_cylindrestaille_moteursysteme_carburanttaux_alésagecoursetaux_compressionchevauxtour_moteurconsommation_villeconsommation_autorouteprix
195196-1volvo 144eagasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415
196197-2volvo 244dlgasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985
197198-1volvo 245gasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515
198199-2volvo 264glgasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420
199200-1volvo dieselgasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950
200201-1volvo 145e (sw)gasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845
201202-1volvo 144eagasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045
202203-1volvo 244dlgasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485
203204-1volvo 246dieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470
204205-1volvo 264glgasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625